CN114092744A - A method and system for classifying and detecting plaques in carotid ultrasound images - Google Patents
A method and system for classifying and detecting plaques in carotid ultrasound images Download PDFInfo
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Abstract
本发明属于图像分类检测领域，提供了一种颈动脉超声图像斑块分类检测方法及系统。其中，该方法包括获取颈动脉横切面视频信息和颈动脉纵切面视频信息；分别对颈动脉横切面视频信息和颈动脉纵切面视频信息进行关键连续帧提取，对所提取的关键连续帧均进行特征增强；对特征增强后的颈动脉横切面图像和颈动脉纵切面图像先进行身份预测，再追踪各个身份对应的图像并进行像素级分割，以实现身份信息与分割结果在时间域上的关联；根据分割结果，遍历掩膜像素的每一列并通过色差标记确定坐标，确定出所述分割结果关联的身份信息对应的斑块大小和面积以及狭窄率；根据斑块大小和面积及狭窄率确定斑块类型，以输出不同程度的预警提示。
The invention belongs to the field of image classification and detection, and provides a carotid artery ultrasound image plaque classification and detection method and system. Wherein, the method includes acquiring the video information of the transverse section of the carotid artery and the video information of the longitudinal section of the carotid artery; extracting key consecutive frames from the video information of the transverse section of the carotid artery and the video information of the longitudinal section of the carotid artery respectively, and extracting the key consecutive frames for all the extracted key consecutive frames. Feature enhancement: first perform identity prediction on the enhanced carotid artery cross-section image and carotid artery longitudinal section image, and then track the images corresponding to each identity and perform pixel-level segmentation to achieve the association between identity information and segmentation results in the time domain ; According to the segmentation result, traverse each column of the mask pixel and determine the coordinates through the color difference mark, determine the patch size and area and the stenosis rate corresponding to the identity information associated with the segmentation result; Determine according to the patch size and area and the stenosis rate Types of plaques to output warning prompts of varying degrees.
Description
Technical Field
The invention belongs to the field of image classification detection, and particularly relates to a carotid artery ultrasonic image plaque classification detection method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Carotid plaque can lead to multiple brain diseases, because plaque has different types, in case vulnerable plaque takes place the breakage, arouses the injury that is difficult to retrieve to the human body, in order to discover the type of plaque so that can in time take precautions against the injury that different plaque changes brought early, needs a carotid plaque classification detecting system, carries out the auxiliary diagnosis, improves diagnostic efficiency.
In carotid ultrasound images, plaques with different sizes and different types exist, the prior art detection generally adopts all plaque label full-supervised learning or no label unsupervised learning, so that the marking workload is increased or the accuracy is insufficient.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a carotid artery ultrasound image plaque classification detection method and system, which can reduce the marking workload of a carotid artery ultrasound image and ensure the accuracy of plaque detection classification.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a carotid artery ultrasonic image plaque classification detection method, which comprises the following steps:
acquiring carotid cross section video information and carotid longitudinal section video information;
respectively extracting key continuous frames from the carotid artery cross section video information and the carotid artery longitudinal section video information, and performing feature enhancement on the extracted key continuous frames;
firstly, identity prediction is carried out on the carotid transverse plane image and the carotid longitudinal plane image after feature enhancement, then, the image corresponding to each identity is tracked and pixel-level segmentation is carried out, so that the correlation of identity information and a segmentation result in a time domain is realized;
traversing each column of the mask pixels according to the segmentation result, determining coordinates through a color difference mark, and determining the size and area of a plaque and the stenosis rate corresponding to the identity information associated with the segmentation result;
and determining the plaque type according to the size and area of the plaque and the stenosis rate so as to output early warning prompts in different degrees.
A second aspect of the present invention provides a carotid artery ultrasound image plaque classification detection system, which includes:
the video information acquisition module is used for acquiring carotid cross section video information and carotid longitudinal section video information;
the characteristic enhancement module is used for respectively extracting key continuous frames from the carotid cross section video information and the carotid longitudinal section video information and carrying out characteristic enhancement on the extracted key continuous frames;
the pixel level segmentation module is used for firstly carrying out identity prediction on the carotid artery cross-section image and the carotid artery longitudinal-section image after the characteristic enhancement, then tracking the image corresponding to each identity and carrying out pixel level segmentation so as to realize the correlation of identity information and a segmentation result in a time domain;
the patch information determining module is used for traversing each column of the mask pixels according to the segmentation result, determining coordinates through the color difference marks, and determining the size, the area and the stenosis rate of a patch corresponding to the identity information associated with the segmentation result;
and the plaque type determining module is used for determining the plaque type according to the plaque size, the plaque area and the stenosis rate so as to output early warning prompts in different degrees.
A third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the carotid artery ultrasound image plaque classification detection method as described above.
A fourth aspect of the present invention provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the carotid artery ultrasound image plaque classification detection method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention respectively extracts key continuous frames from the carotid artery cross section video information and the carotid artery longitudinal section video information, feature enhancement is carried out on the extracted key continuous frames, identity prediction is carried out on the carotid artery cross-section image and the carotid artery longitudinal-section image after feature enhancement, then the image corresponding to each identity is tracked and pixel-level segmentation is carried out, so as to realize the correlation of identity information and segmentation results in a time domain, according to the segmentation result, traversing each column of the mask pixels, determining coordinates through the color difference marks, determining the size, the area and the stenosis rate of the plaque corresponding to the identity information associated with the segmentation result, finally determining the plaque type according to the size, the area and the stenosis rate of the plaque, outputting early warning prompts in different degrees, and not marking a plaque label, so that a large amount of workload is saved, and the accuracy of plaque type detection can be guaranteed.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart of a carotid artery ultrasound image plaque classification detection method according to an embodiment of the invention;
FIG. 2 is a diagram of an exemplary application of the classification and detection of plaque in carotid artery ultrasound images according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a carotid artery ultrasound image plaque classification detection system in an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the present embodiment provides a method for classifying and detecting carotid artery ultrasound image plaque, which specifically includes the following steps:
s101: and acquiring the transverse section video information and the longitudinal section video information of the carotid artery.
In a specific implementation, the carotid artery cross section video information and the carotid artery longitudinal section video information are acquired by an ultrasonic device.
It should be noted that the ultrasound acquisition module includes, but is not limited to, an ultrasound acquisition instrument, a palm ultrasound device, a 5G remote ultrasound acquisition device, and the like.
S102: and respectively extracting key continuous frames from the carotid artery cross-section video information and the carotid artery longitudinal-section video information, and performing feature enhancement on the extracted key continuous frames.
In the specific implementation process, the key continuous frame extraction process is as follows:
and correspondingly dividing the carotid cross-section video information and the carotid longitudinal-section video information into a plurality of sections according to the frame number, judging the number of key frames in each section, and selecting the relevant sections as corresponding continuous key frames.
Specifically, the process of performing feature enhancement on each extracted key continuous frame comprises the following steps:
extracting the characteristics of each key continuous frame;
and denoising the extracted features to realize feature enhancement.
For example: and (4) performing characteristic enhancement such as image transformation denoising, and the like, wherein blind pixel level denoising can be used, a real image denoising method is used for reference, and similar pixels are searched in a global area to enhance the denoising performance.
The algorithm for extracting the features of each key continuous frame comprises but is not limited to an SIFI algorithm, a variance gradient histogram, a Gaussian function difference, MDS, automatic coding of a sparse mode in deep learning and the like;
the algorithm for realizing the feature enhancement includes, but is not limited to, RETINEX image enhancement algorithm, SSD algorithm and the like, and avoids the interference caused by the noise of the ultrasonic machine to the maximum extent.
S103: and performing identity prediction on the carotid cross-section image and the carotid longitudinal-section image after the characteristic enhancement, tracking the image corresponding to each identity, and performing pixel-level segmentation to realize the correlation of identity information and a segmentation result in a time domain.
The method comprises the steps of carrying out pixel level segmentation on images of a cross section and a longitudinal section, adopting an end-to-end instance segmentation method, adding a dynamic tracking task head to predict identity information of an instance in a continuous video frame to realize association in a time domain, and carrying out tracking after detection.
S104: and traversing each column of the mask pixels according to the segmentation result, determining coordinates through the color difference marks, and determining the size and the area of the plaque and the stenosis rate corresponding to the identity information associated with the segmentation result.
In an implementation, based on the determined coordinates, the vertical distances from the left side to the right side and from the upper side to the lower side of the longitudinal section are determined, and the size and the area of the plaque are calculated.
And measuring the normal diameter of the carotid artery and the diameter of the stenosis based on the determined coordinates and the cross section, and calculating the stenosis rate by using a stenosis rate calculation formula.
S105: and determining the plaque type according to the size and area of the plaque and the stenosis rate so as to output early warning prompts in different degrees.
And determining the plaque type based on a pre-trained deep learning network according to the plaque size, the plaque area and the stenosis rate.
The deep learning network includes but is not limited to algorithms such as SVM, AlexNet, ResNet, Faster R-CNN, CNN + LSTM, etc. The plaque types include hard plaque, soft plaque, and mixed plaque.
In the embodiment, the carotid plaque is detected and classified in a weak supervision mode, and is displayed in real time to assist diagnosis, so that the diagnosis efficiency is improved.
As shown in fig. 2, plaque properties are predicted by calculating parameters such as vulnerability index according to the classification result. After plaque detection and classification, real-time display can be carried out, and predicted plaque properties can be displayed.
Example two
As shown in fig. 3, the present embodiment provides a carotid artery ultrasound image plaque classification detection system, which specifically includes the following modules:
a video information obtaining module 201, configured to obtain carotid artery cross-plane video information and carotid artery longitudinal-plane video information;
the feature enhancement module 202 is configured to perform key continuous frame extraction on the carotid artery cross-plane video information and the carotid artery longitudinal-plane video information respectively, and perform feature enhancement on all the extracted key continuous frames;
the pixel level segmentation module 203 is configured to perform identity prediction on the carotid artery cross-plane image and the carotid artery longitudinal-plane image after feature enhancement, and then track the image corresponding to each identity and perform pixel level segmentation to implement association of identity information and a segmentation result in a time domain;
the patch information determining module 204 is configured to traverse each column of the mask pixels according to the segmentation result, determine coordinates through the color difference markers, and determine the size, the area, and the stenosis rate of a patch corresponding to the identity information associated with the segmentation result;
and the plaque type determining module 205 is used for determining the plaque type according to the plaque size, the plaque area and the stenosis rate so as to output early warning prompts in different degrees.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which will not be described again here.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the carotid artery ultrasound image plaque classification detection method as described above.
Among others, a computer-readable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like.
Example four
The present embodiment provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above when executing the program. The carotid artery ultrasonic image plaque classification detection method.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114663372A (en) * | 2022-03-11 | 2022-06-24 | 北京医准智能科技有限公司 | Video-based focus classification method and device, electronic equipment and medium |
| CN115222665A (en) * | 2022-06-13 | 2022-10-21 | 北京医准智能科技有限公司 | Plaque detection method and device, electronic equipment and readable storage medium |
| CN117115187A (en) * | 2023-10-24 | 2023-11-24 | 北京联影智能影像技术研究院 | Carotid artery wall segmentation method, carotid artery wall segmentation device, carotid artery wall segmentation computer device, and carotid artery wall segmentation storage medium |
| US12148158B2 (en) | 2022-10-14 | 2024-11-19 | Qure.Ai Technologies Private Limited | System and method for detecting and quantifying a plaque/stenosis in a vascular ultrasound scan data |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110257545A1 (en) * | 2010-04-20 | 2011-10-20 | Suri Jasjit S | Imaging based symptomatic classification and cardiovascular stroke risk score estimation |
| US20130044931A1 (en) * | 2010-03-26 | 2013-02-21 | The University Of Tokushima | Carotid-artery-plaque ultrasound-imaging method and evaluating device |
| US20130046168A1 (en) * | 2011-08-17 | 2013-02-21 | Lei Sui | Method and system of characterization of carotid plaque |
| CN109409371A (en) * | 2017-08-18 | 2019-03-01 | 三星电子株式会社 | The system and method for semantic segmentation for image |
| CN110246136A (en) * | 2019-05-29 | 2019-09-17 | 山东大学 | A kind of intravascular ultrasound parameter extracting method and system based on hybrid algorithm |
| CN110310271A (en) * | 2019-07-01 | 2019-10-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110428417A (en) * | 2019-08-13 | 2019-11-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110766651A (en) * | 2019-09-05 | 2020-02-07 | 无锡祥生医疗科技股份有限公司 | Carotid plaque property distinguishing method, training method and ultrasonic equipment |
| WO2021097595A1 (en) * | 2019-11-18 | 2021-05-27 | 中国科学院深圳先进技术研究院 | Method and apparatus for segmenting lesion area in image, and server |
| CN113192062A (en) * | 2021-05-25 | 2021-07-30 | 湖北工业大学 | Arterial plaque ultrasonic image self-supervision segmentation method based on image restoration |
-
2021
- 2021-11-26 CN CN202111424971.5A patent/CN114092744B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130044931A1 (en) * | 2010-03-26 | 2013-02-21 | The University Of Tokushima | Carotid-artery-plaque ultrasound-imaging method and evaluating device |
| US20110257545A1 (en) * | 2010-04-20 | 2011-10-20 | Suri Jasjit S | Imaging based symptomatic classification and cardiovascular stroke risk score estimation |
| US20130046168A1 (en) * | 2011-08-17 | 2013-02-21 | Lei Sui | Method and system of characterization of carotid plaque |
| CN109409371A (en) * | 2017-08-18 | 2019-03-01 | 三星电子株式会社 | The system and method for semantic segmentation for image |
| CN110246136A (en) * | 2019-05-29 | 2019-09-17 | 山东大学 | A kind of intravascular ultrasound parameter extracting method and system based on hybrid algorithm |
| CN110310271A (en) * | 2019-07-01 | 2019-10-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110428417A (en) * | 2019-08-13 | 2019-11-08 | 无锡祥生医疗科技股份有限公司 | Property method of discrimination, storage medium and the Vltrasonic device of carotid plaques |
| CN110766651A (en) * | 2019-09-05 | 2020-02-07 | 无锡祥生医疗科技股份有限公司 | Carotid plaque property distinguishing method, training method and ultrasonic equipment |
| WO2021097595A1 (en) * | 2019-11-18 | 2021-05-27 | 中国科学院深圳先进技术研究院 | Method and apparatus for segmenting lesion area in image, and server |
| CN113192062A (en) * | 2021-05-25 | 2021-07-30 | 湖北工业大学 | Arterial plaque ultrasonic image self-supervision segmentation method based on image restoration |
Non-Patent Citations (3)
| Title |
|---|
| 吴秋雯;周书怡;耿辰;李郁欣;曹鑫;耿道颖;杨丽琴;: "基于深度学习的计算机体层摄影血管造影颈动脉斑块分割初步研究", 上海医学, no. 05, 25 May 2020 (2020-05-25) * |
| 张伟;张小龙;赵涓涓;强彦;唐笑先;: "血管粘连型肺结节图像的序列分割方法", 计算机工程与设计, no. 08, 16 August 2018 (2018-08-16) * |
| 蔡梦媛;周然;程新耀;丁明跃;: "基于深度学习的颈动脉超声图像斑块分割算法", 生命科学仪器, no. 03, 25 June 2020 (2020-06-25) * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114663372A (en) * | 2022-03-11 | 2022-06-24 | 北京医准智能科技有限公司 | Video-based focus classification method and device, electronic equipment and medium |
| CN115222665A (en) * | 2022-06-13 | 2022-10-21 | 北京医准智能科技有限公司 | Plaque detection method and device, electronic equipment and readable storage medium |
| US12148158B2 (en) | 2022-10-14 | 2024-11-19 | Qure.Ai Technologies Private Limited | System and method for detecting and quantifying a plaque/stenosis in a vascular ultrasound scan data |
| CN117115187A (en) * | 2023-10-24 | 2023-11-24 | 北京联影智能影像技术研究院 | Carotid artery wall segmentation method, carotid artery wall segmentation device, carotid artery wall segmentation computer device, and carotid artery wall segmentation storage medium |
| CN117115187B (en) * | 2023-10-24 | 2024-02-09 | 北京联影智能影像技术研究院 | Carotid artery wall segmentation method, carotid artery wall segmentation device, carotid artery wall segmentation computer device, and carotid artery wall segmentation storage medium |
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